Premise typicality as feature inference decision-making in perceptual categories

Emma L Morgan,Mark K Johansen

doi:10.3758/s13421-021-01240-8

Abstract

Making property inferences for category instances is important and has been studied in two largely separate areas—categorical induction and perceptual categorization. Categorical induction has a corpus of well-established effects using complex, real-world categories; however, the representational basis of these effects is unclear. In contrast, the perceptual categorization paradigm has fostered the assessment of well-specified representation models due to its controlled stimuli and categories. In categorical induction, evaluations of premise typicality effects, stronger attribute generalization from typical category instances than from atypical, have tried to control the similarity between instances to be distinct from premise–conclusion similarity effects, stronger generalization from greater similarity. However, the extent to which similarity has been controlled is unclear for these complex stimuli. Our research embedded analogues of categorical induction effects in perceptual categories, notably premise typicality and premise conclusion similarity, in an attempt to clarify the category representation underlying feature inference. These experiments controlled similarity between instances using overlap of a small number of constrained features. Participants made inferences for test cases using displayed sets of category instances. The results showed typicality effects, premise–conclusion similarity effects, but no evidence of premise typicality effects (i.e., no preference for generalizing features from typical over atypical category instances when similarity was controlled for), with significant Bayesian support for the null. As typicality effects occurred and occur widely in the perceptual categorization paradigm, why was premise typicality absent? We discuss possible reasons. For attribute inference, is premise typicality distinct from instance similarity? These initial results suggest not.

Highlights

Making property inferences for category instances is important and has been studied in two largely separate areas—categorical induction and perceptual categorization
When interacting with complex environments, categories are adaptively important because they enable the classification of novel objects/events and support subsequent attribute inferences for category instances
The intent here was to establish the existence of effects from the categorical induction paradigm, notably premise typicality, in the more methodologically controlled perceptual

Summary

Introduction

Making property inferences for category instances is important and has been studied in two largely separate areas—categorical induction and perceptual categorization. Categorical induction involves making judgements about unknown features of category instances based on features of known instances, usually for real-world categories (e.g., inferring an instance is edible because other apples have been; Feeney et al, 2007; Gelman & Markman, 1986; Heit, 1998, 2000; López et al, 1992; McDonald et al, 1996; Medin et al, 2003; Medin et al, 1997; Osherson et al, 1990; Proffitt et al, 2000; Rips, 1975, 2001; Sloman, 1993; Smith et al, 1993; Tenenbaum et al, 2006) Research in this paradigm has assessed what properties affect these inferences, ordinarily by using judgements about arguments. Other effects include premise diversity, in which having more diverse category members make stronger arguments, and premise numerosity, in which having more premises makes for stronger arguments

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